Useful R functions

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1] 772 713 643 786 725  56 982 347 950  12 476 867  89 836 296 799  19   6  59 909

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1]   6 347  NA 982 836 786 643 476 867  56 713  12 772  NA 799 909  89  NA 296 725  19  59 950

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 4 5 3 5 2 5 3 4 5 4

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "p" "z" "e" "n" "j" "W" "I" "B" "J" "Y"

Are all/any elements TRUE

• Input: logical vector
• Output: single logical value
• Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1]  7  9 20

which( manyNumbersWithNA > 900 )

[1]  4 16 23

which( is.na( manyNumbersWithNA ) )

[1]  3 14 18

Filtering vector elements

• Input: any vector and filtering condition
• Output: elements of the input vector
• Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 982 950 909

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 982 950 909

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 982 950 909

Are some elements among other elements

• Input: two vectors
• Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "W" "I" "B" "J" "Y"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "p" "z" "e" "n" "j"

manyNumbers %in% 300:600

 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

which( manyNumbers %in% 300:600 )

[1]  8 11

sum( manyNumbers %in% 300:600 )

[1] 2

Pick one of two (three) depending on condition

• Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
• Output: elements of the additional vectors
• Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "small" "small" NA      "large" "large" "large" "large" "small" "large" "small" "large" "small" "large" NA      "large" "large" "small" NA      "small" "large" "small" "small"
[23] "large"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "small"   "small"   "UNKNOWN" "large"   "large"   "large"   "large"   "small"   "large"   "small"   "large"   "small"   "large"   "UNKNOWN" "large"   "large"   "small"   "UNKNOWN"
[19] "small"   "large"   "small"   "small"   "large"  

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L ) 

 [1]   0   0  NA 982 836 786 643   0 867   0 713   0 772  NA 799 909   0  NA   0 725   0   0 950

Duplicates and unique elements

• Input: a vector

unique( duplicatedNumbers )

[1] 4 5 3 2

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  4  5  3  2

duplicated( duplicatedNumbers )

 [1] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 4

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 982

which.min( manyNumbersWithNA )

[1] 1

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 6

range( manyNumbersWithNA, na.rm = TRUE )

[1]   6 982

Sorting/ordering of vectors

manyNumbersWithNA

 [1]   6 347  NA 982 836 786 643 476 867  56 713  12 772  NA 799 909  89  NA 296 725  19  59 950

sort( manyNumbersWithNA )

 [1]   6  12  19  56  59  89 296 347 476 643 713 725 772 786 799 836 867 909 950 982

sort( manyNumbersWithNA, na.last = TRUE )

 [1]   6  12  19  56  59  89 296 347 476 643 713 725 772 786 799 836 867 909 950 982  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 982 950 909 867 836 799 786 772 725 713 643 476 347 296  89  59  56  19  12   6  NA  NA  NA

manyNumbersWithNA[1:5]

[1]   6 347  NA 982 836

order( manyNumbersWithNA[1:5] )

[1] 1 2 5 4 3

rank( manyNumbersWithNA[1:5] )

[1] 1 2 5 4 3

sort( mixedLetters )

 [1] "B" "e" "I" "j" "J" "n" "p" "W" "Y" "z"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1] 9.5 6.5 8.0 6.5 9.5 3.5 5.0 1.5 3.5 1.5

rank( manyDuplicates, ties.method = "min" )

 [1] 9 6 8 6 9 3 5 1 3 1

rank( manyDuplicates, ties.method = "random" )

 [1] 10  6  8  7  9  4  5  2  3  1

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.0000000 -0.5000000  0.0000000  0.5000000  1.0000000 -0.6944255  0.7611939 -0.3308868  1.1798197  1.4555989 -0.4979970 -0.4512086  0.2178149  0.5639085  1.3442934

round( v, 0 )

 [1] -1  0  0  0  1 -1  1  0  1  1  0  0  0  1  1

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0 -0.7  0.8 -0.3  1.2  1.5 -0.5 -0.5  0.2  0.6  1.3

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00 -0.69  0.76 -0.33  1.18  1.46 -0.50 -0.45  0.22  0.56  1.34

floor( v )

 [1] -1 -1  0  0  1 -1  0 -1  1  1 -1 -1  0  0  1

ceiling( v )

 [1] -1  0  0  1  1  0  1  0  2  2  0  0  1  1  2

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177 

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177 

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 x 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b    

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"  

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"  

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